library(corrplot) # for correlation matix
library(stargazer) # visualize model fit
library(skimr) # generate summary statistics
library(car) # statistics 
library(lmtest) # linear modeling
library(sandwich)
library(tidyverse) # for data import, manipulation, viz
filter <- dplyr::filter
data2$acj = data2$prbarr * data2$prbconv * data2$prbpris
data2$west_proper = ifelse(data2$west == 1 & data2$central != 1, 1, 0) 
df = data2 %>% 
  mutate(
    acj = prbarr * prbconv * prbpris
    , west_proper = ifelse(west == 1 & central != 1, 1, 0)
    , central_proper = ifelse(west != 1 & central == 1, 1, 0)
    , num_minorities = density * pctmin80
    , num_ymales = density * pctymle
  ) %>% 
  mutate(
    ln_crmrte = log(crmrte)
    , ln_acj = log(prbarr * prbconv * polpc)
    , ln_wloc = log(wloc)
    , ln_wtrd = log(wtrd)
    , ln_density = log(density)
    , ln_taxpc = log(taxpc)
    , ln_pctymle = log(pctymle)
    ) %>% 
  mutate(
    acj = prbarr * prbconv * polpc
    , west_proper = ifelse(west == 1 & central != 1, 1, 0)
    , central_proper = ifelse(west != 1 & central == 1, 1, 0)
  ) %>% 
  select(
    ln_crmrte
    , ln_acj
    , ln_wloc
    , ln_wtrd
    , ln_taxpc
    , ln_density
    , pctmin80
    , ln_pctymle
    , num_minorities
    , num_ymales
    , urban
    , west_proper
  )
cor(df)
                ln_crmrte      ln_acj     ln_wloc     ln_wtrd    ln_taxpc  ln_density    pctmin80  ln_pctymle num_minorities  num_ymales       urban west_proper
ln_crmrte       1.0000000 -0.39933207  0.30286706  0.38965891  0.33984322  0.49364251  0.23291821  0.31175403      0.6635139  0.66759236  0.49146445 -0.45141763
ln_acj         -0.3993321  1.00000000  0.18125219 -0.04015954 -0.02452981 -0.25189220  0.05632914 -0.24887027     -0.1643648 -0.25482183 -0.16861495  0.15187249
ln_wloc         0.3028671  0.18125219  1.00000000  0.57419433  0.22092405  0.30295286 -0.10213445  0.02258912      0.3918107  0.43137206  0.33004924 -0.15296513
ln_wtrd         0.3896589 -0.04015954  0.57419433  1.00000000  0.16350047  0.42921817 -0.07527956 -0.09784808      0.4839622  0.48656695  0.39889779 -0.19939002
ln_taxpc        0.3398432 -0.02452981  0.22092405  0.16350047  1.00000000  0.10798692  0.02947733 -0.07360943      0.3520511  0.28828811  0.39785937 -0.19215618
ln_density      0.4936425 -0.25189220  0.30295286  0.42921817  0.10798692  1.00000000 -0.09668387  0.17515611      0.4816689  0.56497500  0.39272648 -0.25035461
pctmin80        0.2329182  0.05632914 -0.10213445 -0.07527956  0.02947733 -0.09668387  1.00000000 -0.01224664      0.2848787 -0.05069759  0.01619569 -0.62451443
ln_pctymle      0.3117540 -0.24887027  0.02258912 -0.09784808 -0.07360943  0.17515611 -0.01224664  1.00000000      0.1849915  0.40960966  0.12927006 -0.05212782
num_minorities  0.6635139 -0.16436478  0.39181075  0.48396222  0.35205109  0.48166887  0.28487870  0.18499148      1.0000000  0.88003337  0.80710870 -0.36836902
num_ymales      0.6675924 -0.25482183  0.43137206  0.48656695  0.28828811  0.56497500 -0.05069759  0.40960966      0.8800334  1.00000000  0.81288832 -0.20665165
urban           0.4914645 -0.16861495  0.33004924  0.39889779  0.39785937  0.39272648  0.01619569  0.12927006      0.8071087  0.81288832  1.00000000 -0.08000341
west_proper    -0.4514176  0.15187249 -0.15296513 -0.19939002 -0.19215618 -0.25035461 -0.62451443 -0.05212782     -0.3683690 -0.20665165 -0.08000341  1.00000000

Base model

base_md = lm(log(crmrte) ~ log(acj), data = data2)
plot(base_md)

H0: demographic factors do not have an impact on the model fit

md1 = lm(log(crmrte) ~ log(acj) + log(density) + log(pctymle) + pctmin80, data = data2)
plot(md1)

coeftest(md1)

t test of coefficients:

               Estimate Std. Error t value  Pr(>|t|)    
(Intercept)  -4.9979154  0.5977845 -8.3607 1.061e-12 ***
log(acj)     -0.4636576  0.0607214 -7.6358 3.043e-11 ***
log(density)  0.1787405  0.0269804  6.6248 2.999e-09 ***
log(pctymle)  0.0873188  0.1990884  0.4386    0.6621    
pctmin80      0.0121271  0.0021797  5.5635 3.008e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
coeftest(md1, vcov = vcovHC)

t test of coefficients:

               Estimate Std. Error t value  Pr(>|t|)    
(Intercept)  -4.9979154  0.5727402 -8.7263 1.934e-13 ***
log(acj)     -0.4636576  0.1495965 -3.0994   0.00263 ** 
log(density)  0.1787405  0.2364262  0.7560   0.45173    
log(pctymle)  0.0873188  0.1534316  0.5691   0.57079    
pctmin80      0.0121271  0.0023181  5.2315 1.192e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Density and minority percetange are significant on explaing the variation on crime rate. Bot variables have a positive relationship with crime rate. % of young males does not explain the variation of crime rate.

ggplot(data2, aes(x= pctymle*density , y=log(crmrte + 1))) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)

interac_md2 = lm(log(crmrte) ~ log(acj) + log(pctymle)*log(density) + pctmin80, data = data2)
plot(interac_md2)

coeftest(interac_md2, vcov= vcovHC)

t test of coefficients:

                  Estimate Std. Error  t value  Pr(>|t|)    
(Intercept)     -5.3111217  0.3238046 -16.4022 < 2.2e-16 ***
log(acj)        -0.3427713  0.1046721  -3.2747  0.001537 ** 
pctymle          2.4606492  2.4707490   0.9959  0.322153    
density          0.1974639  0.1243659   1.5878  0.116097    
pctmin80         0.0111803  0.0022048   5.0710 2.328e-06 ***
pctymle:density -0.2173066  1.3803559  -0.1574  0.875285    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
md3 = lm(log(crmrte) ~ log(acj) +  log(density*pctmin80) + log(pctymle), data = data2)
plot(md3)

coeftest(md3, vcov.= vcovHC)

t test of coefficients:

                         Estimate Std. Error t value  Pr(>|t|)    
(Intercept)             -5.030762   0.513855 -9.7902 1.214e-15 ***
log(acj)                -0.411609   0.104502 -3.9388  0.000166 ***
log(density * pctmin80)  0.193798   0.086934  2.2293  0.028404 *  
log(pctymle)             0.112892   0.132557  0.8516  0.396775    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
interact_md4 =  lm(log(crmrte) ~ log(acj) +  density*pctmin80 + log(pctymle), data = data2)
plot(interact_md4)

interac_md5 = lm(log(crmrte) ~ log(acj) + pctymle*density + pctmin80, data = data2)
plot(interac_md5)

se = sqrt(diag(vcovHC(base_md)))
se1 = sqrt(diag(vcovHC(md1)))
se2 = sqrt(diag(vcovHC(interac_md2)))
se3 = sqrt(diag(vcovHC(md3)))
se4 = sqrt(diag(vcovHC(interact_md4)))
se5 = sqrt(diag(vcovHC(interac_md5)))
stargazer(
  base_md
  , md1
  , interac_md2
  , md3
  , interact_md4
  , interac_md5
  , type = "text"
  , se = list(se, se1, se2, se3, se4, se5)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)

===================================================================================================================================================================
                                                                                     Dependent variable:                                                           
                          -----------------------------------------------------------------------------------------------------------------------------------------
                                                                                         log(crmrte)                                                               
                                   (1)                    (2)                    (3)                    (4)                    (5)                    (6)          
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
log(acj)                        -0.470***               -0.464**               -0.440**              -0.412***               -0.341**               -0.343**       
                                 (0.106)                (0.150)                (0.135)                (0.105)                (0.104)                (0.105)        
                                                                                                                                                                   
log(density)                                             0.179                  1.774                                                                              
                                                        (0.236)                (1.610)                                                                             
                                                                                                                                                                   
log(density * pctmin80)                                                                                0.194*                                                      
                                                                                                      (0.087)                                                      
                                                                                                                                                                   
pctymle                                                                                                                                              2.461         
                                                                                                                                                    (2.471)        
                                                                                                                                                                   
density                                                                                                                      0.232**                 0.197         
                                                                                                                             (0.083)                (0.124)        
                                                                                                                                                                   
log(pctymle)                                             0.087                  -0.107                 0.113                  0.273                                
                                                        (0.153)                (0.253)                (0.133)                (0.160)                               
                                                                                                                                                                   
density:pctmin80                                                                                                              -0.002                               
                                                                                                                             (0.003)                               
                                                                                                                                                                   
pctmin80                                                0.012***               0.012***                                      0.013***               0.011***       
                                                        (0.002)                (0.003)                                       (0.004)                (0.002)        
                                                                                                                                                                   
log(pctymle):log(density)                                                       0.620                                                                              
                                                                               (0.698)                                                                             
                                                                                                                                                                   
pctymle:density                                                                                                                                      -0.217        
                                                                                                                                                    (1.380)        
                                                                                                                                                                   
Constant                        -4.937***              -4.998***              -5.424***              -5.031***              -4.467***              -5.311***       
                                 (0.296)                (0.573)                (0.865)                (0.514)                (0.575)                (0.324)        
                                                                                                                                                                   
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Observations                        90                     90                     90                     90                     90                     90          
R2                                0.315                  0.629                  0.648                  0.663                  0.652                  0.650         
Adjusted R2                       0.307                  0.612                  0.627                  0.651                  0.631                  0.629         
Residual Std. Error          0.457 (df = 88)        0.342 (df = 85)        0.335 (df = 84)        0.324 (df = 86)        0.333 (df = 84)        0.334 (df = 84)    
F Statistic               40.481*** (df = 1; 88) 36.066*** (df = 4; 85) 30.864*** (df = 5; 84) 56.390*** (df = 3; 86) 31.479*** (df = 5; 84) 31.211*** (df = 5; 84)
===================================================================================================================================================================
Note:                                                                                                                                 *p<0.05; **p<0.01; ***p<0.001
demo_md1 = lm(log(crmrte) ~ log(acj) + pctmin80, data = data2)
plot(demo_md1)

demo_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80), data = data2)
plot(demo_md2)

s2_demo1 = sqrt(diag(vcovHC(demo_md1)))
s2_demo2 = sqrt(diag(vcovHC(demo_md2)))
stargazer(
  base_md
  , demo_md1
  , demo_md2
  , type = "text"
  , se = list(se, s2_demo1, s2_demo2)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)

============================================================================================
                                                Dependent variable:                         
                        --------------------------------------------------------------------
                                                    log(crmrte)                             
                                 (1)                    (2)                    (3)          
--------------------------------------------------------------------------------------------
log(acj)                      -0.470***              -0.521***              -0.424***       
                               (0.106)                (0.093)                (0.097)        
                                                                                            
pctmin80                                              0.011***                              
                                                      (0.002)                               
                                                                                            
log(density * pctmin80)                                                       0.195*        
                                                                             (0.087)        
                                                                                            
Constant                      -4.937***              -5.375***              -5.354***       
                               (0.296)                (0.249)                (0.227)        
                                                                                            
--------------------------------------------------------------------------------------------
Observations                      90                     90                     90          
R2                              0.315                  0.430                  0.662         
Adjusted R2                     0.307                  0.416                  0.654         
Residual Std. Error        0.457 (df = 88)        0.419 (df = 87)        0.323 (df = 87)    
F Statistic             40.481*** (df = 1; 88) 32.759*** (df = 2; 87) 85.029*** (df = 2; 87)
============================================================================================
Note:                                                          *p<0.05; **p<0.01; ***p<0.001
geo_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + west_proper, data = data2)
plot(geo_md1)

geo_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + west_proper + urban, data = data2)
plot(geo_md2)

geo_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + urban, data = data2)
plot(geo_md3)

se_geo1 = sqrt(diag(vcovHC(geo_md1)))
se_geo2 = sqrt(diag(vcovHC(geo_md2)))
se_geo3 = sqrt(diag(vcovHC(geo_md3)))
stargazer(
  demo_md2
  , geo_md1
  , geo_md2
  , geo_md3
  , type = "text"
  , se = list(s2_demo2, se_geo1, se_geo2)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)

===================================================================================================================
                                                            Dependent variable:                                    
                        -------------------------------------------------------------------------------------------
                                                                log(crmrte)                                        
                                 (1)                    (2)                    (3)                    (4)          
-------------------------------------------------------------------------------------------------------------------
log(acj)                      -0.424***              -0.423***              -0.385***              -0.392***       
                               (0.097)                (0.100)                (0.087)                (0.054)        
                                                                                                                   
log(density * pctmin80)         0.195*                 0.185                  0.157                 0.179***       
                               (0.087)                (0.138)                (0.161)                (0.022)        
                                                                                                                   
west_proper                                            -0.062                 -0.123                               
                                                      (0.248)                (0.295)                               
                                                                                                                   
urban                                                                         0.302                  0.261         
                                                                             (0.294)                (0.134)        
                                                                                                                   
Constant                      -5.354***              -5.308***              -5.127***              -5.236***       
                               (0.227)                (0.347)                (0.495)                (0.173)        
                                                                                                                   
-------------------------------------------------------------------------------------------------------------------
Observations                      90                     90                     90                     90          
R2                              0.662                  0.663                  0.681                  0.676         
Adjusted R2                     0.654                  0.651                  0.666                  0.665         
Residual Std. Error        0.323 (df = 87)        0.324 (df = 86)        0.317 (df = 85)        0.318 (df = 86)    
F Statistic             85.029*** (df = 2; 87) 56.375*** (df = 3; 86) 45.339*** (df = 4; 85) 59.800*** (df = 3; 86)
===================================================================================================================
Note:                                                                                 *p<0.05; **p<0.01; ***p<0.001
mix_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(mix), data = data2)
plot(mix_md1)

se_mix1 = sqrt(diag(vcovHC(mix_md1)))
stargazer(
  demo_md2
  , mix_md1
  , type = "text"
  , se = list(s2_demo2, se_mix1)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)

=====================================================================
                                     Dependent variable:             
                        ---------------------------------------------
                                         log(crmrte)                 
                                 (1)                    (2)          
---------------------------------------------------------------------
log(acj)                      -0.424***              -0.423***       
                               (0.097)                (0.096)        
                                                                     
log(density * pctmin80)         0.195*                 0.199*        
                               (0.087)                (0.084)        
                                                                     
log(mix)                                               0.078         
                                                      (0.092)        
                                                                     
Constant                      -5.354***              -5.191***       
                               (0.227)                (0.356)        
                                                                     
---------------------------------------------------------------------
Observations                      90                     90          
R2                              0.662                  0.667         
Adjusted R2                     0.654                  0.656         
Residual Std. Error        0.323 (df = 87)        0.322 (df = 86)    
F Statistic             85.029*** (df = 2; 87) 57.537*** (df = 3; 86)
=====================================================================
Note:                                   *p<0.05; **p<0.01; ***p<0.001
eco_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc), data = data2)
plot(eco_md1)

eco_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc), data = data2)
plot(eco_md2 )

eco_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc) + log(taxpc), data = data2)
plot(eco_md3)

eco_md4 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc)*log(taxpc), data = data2)
plot(eco_md4)

eco_md5 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc + taxpc^2, data = data2)
plot(eco_md5)

eco_md6 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + wloc + wloc^2, data = data2)
plot(eco_md6)

eco_md7 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc)*log(wloc), data = data2)
plot(eco_md7)

se_eco1 = sqrt(diag(vcovHC(eco_md1)))
se_eco2 = sqrt(diag(vcovHC(eco_md2)))
se_eco3 = sqrt(diag(vcovHC(eco_md3)))
se_eco4 = sqrt(diag(vcovHC(eco_md4)))
se_eco5 = sqrt(diag(vcovHC(eco_md5)))
se_eco6 = sqrt(diag(vcovHC(eco_md6)))
se_eco7 = sqrt(diag(vcovHC(eco_md7)))
stargazer(
   eco_md1
  , eco_md2
  , eco_md3
  , eco_md4
  , eco_md5
  , type = "text"
  , se = list(s2_demo2, se_eco1, se_eco2, se_eco3, se_eco4, se_eco5)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)

==========================================================================================================================================
                                                                       Dependent variable:                                                
                        ------------------------------------------------------------------------------------------------------------------
                                                                           log(crmrte)                                                    
                                 (1)                    (2)                    (3)                    (4)                    (5)          
------------------------------------------------------------------------------------------------------------------------------------------
log(acj)                      -0.398***              -0.428***              -0.408***              -0.397***              -0.388***       
                               (0.097)                (0.089)                (0.089)                (0.083)                (0.083)        
                                                                                                                                          
log(density * pctmin80)         0.190*                 0.181*                 0.179*                 0.172*                 0.190*        
                               (0.087)                (0.085)                (0.084)                (0.083)                (0.083)        
                                                                                                                                          
log(taxpc)                      0.254                                         0.192                -13.765***                             
                                                                                                    (0.177)                               
                                                                                                                                          
log(wloc):log(taxpc)                                                                                 2.417                                
                                                                                                                                          
                                                                                                                                          
log(wloc)                                              1.004                  0.890                -7.712***                              
                                                                             (0.515)                (0.494)                               
                                                                                                                                          
taxpc                                                                                                                       0.007         
                                                                                                                                          
                                                                                                                                          
Constant                      -6.178***              -11.089***             -11.061***             38.665***                -5.491        
                               (0.227)                (0.661)                (2.867)                (2.801)                (40.400)       
                                                                                                                                          
------------------------------------------------------------------------------------------------------------------------------------------
Observations                      90                     90                     90                     90                     90          
R2                              0.675                  0.687                  0.694                  0.703                  0.685         
Adjusted R2                     0.664                  0.676                  0.680                  0.686                  0.674         
Residual Std. Error        0.318 (df = 86)        0.312 (df = 86)        0.310 (df = 85)        0.308 (df = 84)        0.313 (df = 86)    
F Statistic             59.628*** (df = 3; 86) 62.872*** (df = 3; 86) 48.279*** (df = 4; 85) 39.810*** (df = 5; 84) 62.469*** (df = 3; 86)
==========================================================================================================================================
Note:                                                                                                        *p<0.05; **p<0.01; ***p<0.001
stargazer(
  eco_md6
  , eco_md7
  , type = "text"
  , se = list(s2_demo2, se_eco5, se_eco6)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed

============================================================
                                   Dependent variable:      
                              ------------------------------
                                       log(crmrte)          
                                    (1)            (2)      
------------------------------------------------------------
log(acj)                         -0.429***      -0.428***   
                                  (0.097)        (0.090)    
                                                            
log(density * pctmin80)           0.182*          0.181*    
                                  (0.087)        (0.085)    
                                                            
wloc                               0.003                    
                                                            
                                                            
log(wloc)                                         1.004     
                                                            
                                                            
Constant                         -6.294***      -11.089***  
                                  (0.227)        (0.234)    
                                                            
------------------------------------------------------------
Observations                        90              90      
R2                                 0.685          0.687     
Adjusted R2                        0.674          0.676     
Residual Std. Error (df = 86)      0.313          0.312     
F Statistic (df = 3; 86)         62.304***      62.872***   
============================================================
Note:                          *p<0.05; **p<0.01; ***p<0.001
m4 <- lm(log(crmrte) ~ log(acj) + log(avgsen) + log(density) + mix  + pctymle + pctmin80 + west + central + urban  + log(taxpc) , data = data2) 
combined_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc) + urban, data = data2)
plot(combined_md1)

combined_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc) + urban, data = data2)
plot(combined_md1)

combined_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc*taxpc + urban, data = data2)
plot(combined_md2)

combined_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc*taxpc + wloc*wloc + urban, data = data2)
plot(combined_md3)

se_m4 = sqrt(diag(vcovHC(m4)))
se_combined_md1 = sqrt(diag(vcovHC(combined_md1)))
se_combined_md2 = sqrt(diag(vcovHC(combined_md2)))
se_combined_md3 = sqrt(diag(vcovHC(combined_md3)))
stargazer(
  demo_md2
  , m4
  , combined_md1
  , combined_md2
  , combined_md3
  , type = "text"
  , se = list(s2_demo2, se_m4, se_combined_md1)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changednumber of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)number of rows of result is not a multiple of vector length (arg 2)

===========================================================================================================================================
                                                                        Dependent variable:                                                
                        -------------------------------------------------------------------------------------------------------------------
                                                                            log(crmrte)                                                    
                                 (1)                     (2)                    (3)                    (4)                    (5)          
-------------------------------------------------------------------------------------------------------------------------------------------
log(acj)                      -0.424***               -0.384**               -0.380***              -0.370***              -0.384***       
                               (0.097)                 (0.132)                (0.088)                (0.054)                (0.054)        
                                                                                                                                           
log(density * pctmin80)         0.195*                                         0.179*                0.180***               0.172***       
                               (0.087)                                        (0.091)                (0.022)                (0.022)        
                                                                                                                                           
log(avgsen)                                            -0.068                                                                              
                                                       (0.159)                                                                             
                                                                                                                                           
log(density)                                            0.142                                                                              
                                                       (0.346)                                                                             
                                                                                                                                           
mix                                                    -0.053                                                                              
                                                       (0.715)                                                                             
                                                                                                                                           
pctymle                                                 1.690                                                                              
                                                       (1.513)                                                                             
                                                                                                                                           
pctmin80                                                0.009                                                                              
                                                       (0.005)                                                                             
                                                                                                                                           
west                                                   -0.126                                                                              
                                                       (0.191)                                                                             
                                                                                                                                           
central                                                 0.009                                                                              
                                                       (0.201)                                                                             
                                                                                                                                           
taxpc                                                                                                 0.006*                 0.005         
                                                                                                     (0.003)                (0.003)        
                                                                                                                                           
wloc                                                                                                                         0.002         
                                                                                                                            (0.001)        
                                                                                                                                           
wtrd                                                                                                                         0.001         
                                                                                                                            (0.001)        
                                                                                                                                           
urban                                                   0.289                  0.201                  0.185                  0.105         
                                                       (0.533)                (0.206)                (0.136)                (0.143)        
                                                                                                                                           
log(taxpc)                                              0.210                  0.191                                                       
                                                       (0.235)                (0.234)                                                      
                                                                                                                                           
Constant                      -5.354***               -5.640***              -5.883***              -5.387***              -6.096***       
                               (0.227)                 (0.985)                (0.916)                (0.184)                (0.437)        
                                                                                                                                           
-------------------------------------------------------------------------------------------------------------------------------------------
Observations                      90                     90                      90                     90                     90          
R2                              0.662                   0.669                  0.683                  0.692                  0.704         
Adjusted R2                     0.654                   0.627                  0.668                  0.678                  0.683         
Residual Std. Error        0.323 (df = 87)         0.335 (df = 79)        0.316 (df = 85)        0.312 (df = 85)        0.309 (df = 83)    
F Statistic             85.029*** (df = 2; 87) 15.969*** (df = 10; 79) 45.775*** (df = 4; 85) 47.771*** (df = 4; 85) 32.959*** (df = 6; 83)
===========================================================================================================================================
Note:                                                                                                         *p<0.05; **p<0.01; ***p<0.001
plot(log(data2$crmrte), combined_md1$fitted.values, main = "Crime Rate - Actual vs Predicted")
abline(a=0,b=1)

---
title: "R Notebook"
output: html_notebook
---

```{r, message=FALSE}
library(corrplot) # for correlation matix
library(stargazer) # visualize model fit
library(skimr) # generate summary statistics
library(car) # statistics 
library(lmtest) # linear modeling
library(sandwich)
library(tidyverse) # for data import, manipulation, viz
filter <- dplyr::filter
```

```{r}
data2$acj = data2$prbarr * data2$prbconv * data2$prbpris
data2$west_proper = ifelse(data2$west == 1 & data2$central != 1, 1, 0) 
```


```{r}
df = data2 %>% 
  mutate(
    acj = prbarr * prbconv * prbpris
    , west_proper = ifelse(west == 1 & central != 1, 1, 0)
    , central_proper = ifelse(west != 1 & central == 1, 1, 0)
    , num_minorities = density * pctmin80
    , num_ymales = density * pctymle
  ) %>% 
  mutate(
    ln_crmrte = log(crmrte)
    , ln_acj = log(prbarr * prbconv * polpc)
    , ln_wloc = log(wloc)
    , ln_wtrd = log(wtrd)
    , ln_density = log(density)
    , ln_taxpc = log(taxpc)
    , ln_pctymle = log(pctymle)
    ) %>% 
  mutate(
    acj = prbarr * prbconv * polpc
    , west_proper = ifelse(west == 1 & central != 1, 1, 0)
    , central_proper = ifelse(west != 1 & central == 1, 1, 0)
  ) %>% 
  select(
    ln_crmrte
    , ln_acj
    , ln_wloc
    , ln_wtrd
    , ln_taxpc
    , ln_density
    , pctmin80
    , ln_pctymle
    , num_minorities
    , num_ymales
    , urban
    , west_proper
  )
cor(df)
```

Base model
```{r}
base_md = lm(log(crmrte) ~ log(acj), data = data2)
plot(base_md)
```

H0: demographic factors do not have an impact on the model fit
```{r}
md1 = lm(log(crmrte) ~ log(acj) + log(density) + log(pctymle) + pctmin80, data = data2)
plot(md1)
```

```{r}
coeftest(md1)
```

```{r}
coeftest(md1, vcov = vcovHC)
```

Density and minority percetange are significant on explaing the variation on crime rate. Bot variables have a positive relationship with crime rate. % of young males does not explain the variation of crime rate.

```{r}
ggplot(data2, aes(x= pctymle*density , y=log(crmrte + 1))) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)
```

```{r}
interac_md2 = lm(log(crmrte) ~ log(acj) + log(pctymle)*log(density) + pctmin80, data = data2)
plot(interac_md2)
```

```{r}
coeftest(interac_md2, vcov= vcovHC)
```

```{r}
md3 = lm(log(crmrte) ~ log(acj) +  log(density*pctmin80) + log(pctymle), data = data2)
plot(md3)
```
```{r}
coeftest(md3, vcov.= vcovHC)
```

```{r}
interact_md4 =  lm(log(crmrte) ~ log(acj) +  density*pctmin80 + log(pctymle), data = data2)
plot(interact_md4)
```

```{r}
interac_md5 = lm(log(crmrte) ~ log(acj) + pctymle*density + pctmin80, data = data2)
plot(interac_md5)
```


```{r}
se = sqrt(diag(vcovHC(base_md)))
se1 = sqrt(diag(vcovHC(md1)))
se2 = sqrt(diag(vcovHC(interac_md2)))
se3 = sqrt(diag(vcovHC(md3)))
se4 = sqrt(diag(vcovHC(interact_md4)))
se5 = sqrt(diag(vcovHC(interac_md5)))
```

```{r}
stargazer(
  base_md
  , md1
  , interac_md2
  , md3
  , interact_md4
  , interac_md5
  , type = "text"
  , se = list(se, se1, se2, se3, se4, se5)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```

```{r}
demo_md1 = lm(log(crmrte) ~ log(acj) + pctmin80, data = data2)
plot(demo_md1)
```

```{r}
demo_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80), data = data2)
plot(demo_md2)
```

```{r}
s2_demo1 = sqrt(diag(vcovHC(demo_md1)))
s2_demo2 = sqrt(diag(vcovHC(demo_md2)))
```

```{r}
stargazer(
  base_md
  , demo_md1
  , demo_md2
  , type = "text"
  , se = list(se, s2_demo1, s2_demo2)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```

```{r}
geo_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + west_proper, data = data2)
plot(geo_md1)
```

```{r}
geo_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + west_proper + urban, data = data2)
plot(geo_md2)
```

```{r}
geo_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + urban, data = data2)
plot(geo_md3)
```

```{r}
se_geo1 = sqrt(diag(vcovHC(geo_md1)))
se_geo2 = sqrt(diag(vcovHC(geo_md2)))
se_geo3 = sqrt(diag(vcovHC(geo_md3)))
```

```{r}
stargazer(
  demo_md2
  , geo_md1
  , geo_md2
  , geo_md3
  , type = "text"
  , se = list(s2_demo2, se_geo1, se_geo2)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```

```{r}
mix_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(mix), data = data2)
plot(mix_md1)
```

```{r}
se_mix1 = sqrt(diag(vcovHC(mix_md1)))
```


```{r}
stargazer(
  demo_md2
  , mix_md1
  , type = "text"
  , se = list(s2_demo2, se_mix1)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```

```{r}
eco_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc), data = data2)
plot(eco_md1)
```

```{r}
eco_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc), data = data2)
plot(eco_md2 )
```

```{r}
eco_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc) + log(taxpc), data = data2)
plot(eco_md3)
```

```{r}
eco_md4 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc)*log(taxpc), data = data2)
plot(eco_md4)
```

```{r}
eco_md5 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc + taxpc^2, data = data2)
plot(eco_md5)
```

```{r}
eco_md6 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + wloc + wloc^2, data = data2)
plot(eco_md6)
```

```{r}
eco_md7 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(wloc)*log(wloc), data = data2)
plot(eco_md7)
```


```{r}
se_eco1 = sqrt(diag(vcovHC(eco_md1)))
se_eco2 = sqrt(diag(vcovHC(eco_md2)))
se_eco3 = sqrt(diag(vcovHC(eco_md3)))
se_eco4 = sqrt(diag(vcovHC(eco_md4)))
se_eco5 = sqrt(diag(vcovHC(eco_md5)))
se_eco6 = sqrt(diag(vcovHC(eco_md6)))
se_eco7 = sqrt(diag(vcovHC(eco_md7)))
```

```{r}
stargazer(
   eco_md1
  , eco_md2
  , eco_md3
  , eco_md4
  , eco_md5
  , type = "text"
  , se = list(s2_demo2, se_eco1, se_eco2, se_eco3, se_eco4, se_eco5)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```


```{r}
stargazer(
  eco_md6
  , eco_md7
  , type = "text"
  , se = list(s2_demo2, se_eco5, se_eco6)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```


```{r}
m4 <- lm(log(crmrte) ~ log(acj) + log(avgsen) + log(density) + mix  + pctymle + pctmin80 + west + central + urban  + log(taxpc) , data = data2) 
```

```{r}
combined_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc) + urban, data = data2)
plot(combined_md1)
```

```{r}
combined_md1 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + log(taxpc) + urban, data = data2)
plot(combined_md1)
```

```{r}
combined_md2 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc*taxpc + urban, data = data2)
plot(combined_md2)
```

```{r}
combined_md3 = lm(log(crmrte) ~ log(acj) + log(density * pctmin80) + taxpc*taxpc + wloc*wloc + urban, data = data2)
plot(combined_md3)
```

```{r}
se_m4 = sqrt(diag(vcovHC(m4)))
se_combined_md1 = sqrt(diag(vcovHC(combined_md1)))
se_combined_md2 = sqrt(diag(vcovHC(combined_md2)))
se_combined_md3 = sqrt(diag(vcovHC(combined_md3)))
```

```{r}
stargazer(
  demo_md2
  , m4
  , combined_md1
  , combined_md2
  , combined_md3
  , type = "text"
  , se = list(s2_demo2, se_m4, se_combined_md1)
  , star.cutoffs = c(0.05, 0.01, 0.001)
)
```

```{r}
plot(log(data2$crmrte), combined_md1$fitted.values, main = "Crime Rate - Actual vs Predicted")
abline(a=0,b=1)
```

